PhD on Identification of constitutive laws in engineering systems

The modeling of complex engineering systems is highly challenging. Physics-based models require a cautious application of constitutive assumptions, whereas data-based models require vast amounts of data. The research project "DAMOCLES; Data-Augmented Modeling Of Constructive Laws for Engineering Systems", of which this PhD position is a part, targets a breakthrough in the constitutive modeling of such systems in different physical domains by developing a unified multi-tool framework that combines the favorable characteristics of physics-based and data-based approaches. DAMOCLES is an inter-departmental project part of the

Eindhoven Artificial Intelligence Institute (EAISI)

Exploratory Multidisciplinary AI Research Program (EMDAIR).

The DAMOCLES project is divided into three interlinked sub-projects of which one project is advertised here. The other DAMOCLES vacancies are:

This open PhD position aims to construct a robust constitutive modeling framework from a set of available state-of-the-art phenomenological models that can adopt to a wide range of material characteristics of different physics, while emulating various levels of nonlinearity and hysteresis, such that the properties of complex systems can be identified in a noninvasive way. 

Constitutive laws of materials mimic the observed microscopic phenomena at the macroscopic level. The appropriate material model is quantified by fitting its parameters to mimic the material response in a specific engineering application. Furthermore, the data which are usually acquired on simplified geometries, or samples, are far from representative for the final designs. Due to the manufacturing process (heating, cutting, stresses after assembly), large discrepancies are expected between the sample-predicted and the effective material characteristics of the final design. The PhD candidate will develop methods and identification algorithms which will characterize the material behavior in engineering system with increasing complexity. Special focus is dedicated to material responses in magnetic, electric (piezo) and mechanical domains subject to hysteresis behavior. The candidate will adopt existing state-of-art models possessing necessary properties such as congruency, rate-dependency or memory and recombine them for a specific application using evolutionary algorithms.

To accomplish the objectives of the DAMOCLES project, a strong cooperation with the PhD candidates and researchers in the other sub-projects is required. In particular, the results obtained in this PhD project will be evaluated and analyzed on a selected set of overarching benchmark applications.

Tasks

• Literature study of system identification and constitutive modeling with focus on hysteresis.
• Establish an environment where the constitutive models are interpretable and allow for an efficient search space exploration.
• Employ evolutionary algorithms to recombine the models targeting a specific application.
• Exploration of the identification steps for the developed methods and design of experiments for validation purposes.
• Dissemination of the results of your research in international and peer-reviewed journals and conferences.
• Writing a successful dissertation based on the developed research and defending it.
• Assume educational tasks like the supervision of Master students and internships.
• Successful integration in the Eindhoven Artificial Intelligence for Systems Institute.